Textured surfaces offer the potential to promote friction and wear reduction by increasing the hydrodynamic pressure, fluid uptake, or acting as oil or debris reservoirs. However, texturing techniques often require additional manufacturing steps and costs, thus frequently being not economically feasible for real engineering applications. This experimental study aims at applying a fast laser texturing technique on curved surfaces for obtaining superior tribological performances. A femtosecond pulsed laser (Ti:Sapphire) and direct laser interference patterning (with a solid-state Nd:YAG laser) were used for manufacturing dimple and groove patterns on curved steel surfaces (ball samples). Tribological tests were carried out under elasto-hydrodynamic lubricated contact conditions varying slide-roll ratio using a ball-on-disk configuration. Furthermore, a specific interferometry technique for rough surfaces was used to measure the film thickness of smooth and textured surfaces. Smooth steel samples were used to obtain data for the reference surface. The results showed that dimples promoted friction reduction (up to 20%) compared to the reference smooth specimens, whereas grooves generally caused less beneficial or detrimental effects. In addition, dimples promoted the formation of full film lubrication conditions at lower speeds. This study demonstrates how fast texturing techniques could potentially be used for improving the tribological performance of bearings as well as other mechanical components utilised in several engineering applications.

The coefficient of friction (CoF) obtained from tribological tests conducted on textured and porous surfaces was analysed using the machine learning Radial Basis Function (RBF) method. Non-textured and non-porous samples were taken as reference surfaces. Test parameters, such as entrainment velocity and slide-roll ratio (SRR), along with geometric characteristics of surface features (e.g. texture width and depth, coverage area, circularity, spatial distribution and directionality, among others), were selected as training dataset for the machine learning RBF model. The surface features were divided into designed patterns (dimples and grooves) manufactured by laser texturing, and randomised cavities (surface pores) resulted from the sintering process. The principal outcomes of this study are the effective use of the machine learning RBF method for tribological applications, as well as a critical discussion on its feasibility for the experimental dataset selected and the preliminary results obtained. Main results show that the Hardy multiquadric radial basis function provided an overall correlation coefficient of 0.934 for 35 poles. The application of the suggested machine learning technique and methodology can be extended to other experimental results available in the literature to train more robust models for predicting tribological performances of textured and structured surfaces.